This is a brief comparison of definitions for “middle neighborhoods” in Baltimore. The two definitions being compared are:

U.S. Census data being used are the 2012-2016 (5-year) American Community Survey estimates.

Are there any other definitions we should consider?

# neighborhood boundaries
hoods.url <- "https://data.baltimorecity.gov/resource/h3fx-54q3.geojson"
# surely there is a better way, and i tried, but couldn't get anything else to work.
# from geojson to sf to spatial df. geojson_sp didn't work for me.
hoods <- geojson_sf(hoods.url)
incomplete final line found on 'https://data.baltimorecity.gov/resource/h3fx-54q3.geojson'
hoods <- as_Spatial(hoods)
hoods <- spTransform(hoods, CRS("+init=epsg:4326"))
# HUD area median income for Baltimore-Columbia-Towson
ami.2016 <- 86700
#ami.2018 <- 94900
hmt@data <- hmt@data %>% mutate(
  
  # assign healthy, middle, and distressed based on HMT
  hmt.tier = case_when(
    MVA17HrdCd %in% c("A", "B", "C") ~ "healthy",
    MVA17HrdCd %in% c("D", "E", "F", "G", "H") ~ "middle",
    MVA17HrdCd %in% c("I", "J") ~ "distressed",
    TRUE ~ "other"),
  
  # HMT again, but with middle broken in two
  hmt.tier.2mid = case_when(
    MVA17HrdCd %in% c("A", "B", "C") ~ "healthy",
    MVA17HrdCd %in% c("D", "E") ~ "upper middle",
    MVA17HrdCd %in% c("F", "G", "H") ~ "lower middle",
    MVA17HrdCd %in% c("I", "J") ~ "distressed",
    TRUE ~ "other"),
  # assign healthy, middle, and distressed based on block group median income
    med.inc.tier = 
    case_when(
      (hmt.tier != "other") & 
        (Median_Household_Income.2016 > 1.25 * ami.2016) ~ "healthy",
      (hmt.tier != "other") & 
        (Median_Household_Income.2016 < 0.75 * ami.2016) ~ "distressed",
      (hmt.tier == "other") ~ NA_character_,
      TRUE ~ "middle"
    )
  )

Middle Neighborhoods - HMT Definiton

This map shows only middle neighborhoods based on HMT (includes only typologies D through H). Black borders and labels-on-hover are for neighborhoods. At first glance, this removes neighborhoods within the “white L” and “black butterfly” neighborhoods of Baltimore, with most everything else remaining.

mid.hoods.hmt <- subset(hmt, hmt.tier == "middle")
leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = mid.hoods.hmt, 
              color = iteam.colors[1],
              weight = 1,
              #fillColor = ~pal(MVA17HrdCd),
              fillColor = iteam.colors[1],
              fillOpacity = .6,
              #opacity = 0,
              label = ~as.character(mid.hoods.hmt$MVA17HrdCd)
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~hoods$label)

Breaking “middle” into “lower middle” (F, G, H) and “upper middle” (D, E) and including “healthy” and "distressed:

#mid.hoods.hmt.2mid <- subset(hmt, hmt.tier.2mid %in% c("lower middle", "upper middle"))
hmt.2mid.pal <- colorFactor(
  domain = hmt$hmt.tier.2mid,
  palette = c(iteam.colors[4],
              iteam.colors[3],
              "#f4d57f", 
              NA,
              iteam.colors[1])
)
leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = hmt, 
              color = iteam.colors[1],
              weight = 1,
              fillColor = ~hmt.2mid.pal(hmt.tier.2mid),
              #fillColor = iteam.colors[1],
              fillOpacity = .6,
              #opacity = 0,
              label = ~as.character(hmt$MVA17HrdCd)
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~hoods$label)

Middle Neighborhoods - Median Income Definition

This map shows only middle neighborhoods based on the median income definition - that is, block groups where the household median income is between 75% and 125% of the area median income (Baltimore-Columbia-Towson for 2016). Qualitatively, this results in a more sparse and disjointed definition of middle neighborhoods.

mid.hoods.med.inc <- subset(hmt, med.inc.tier == "middle")
leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = mid.hoods.med.inc, 
              color = iteam.colors[3],
              weight = 1,
              #fillColor = ~pal(MVA17HrdCd),
              fillColor = iteam.colors[3],
              fillOpacity = .6,
              #opacity = 0,
              label = ~as.character(mid.hoods.med.inc$MVA17HrdCd)
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~hoods$label)

Comparison of Definitions

The map below overlays the two definitions.

leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = mid.hoods.med.inc, 
              color = iteam.colors[3],
              weight = 1,
              #fillColor = ~pal(MVA17HrdCd),
              fillColor = iteam.colors[3],
              fillOpacity = .7,
              #opacity = 0,
              label = ~as.character(mid.hoods.med.inc$MVA17HrdCd)) %>%
  addPolygons(data = mid.hoods.hmt, 
              color = iteam.colors[1],
              weight = 1,
              #fillColor = ~pal(MVA17HrdCd),
              fillColor = iteam.colors[1],
              fillOpacity = .5,
              #opacity = 0,
              label = ~as.character(mid.hoods.hmt$MVA17HrdCd)) %>%
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~hoods$label) %>%
  addLegend("bottomright",
            colors = c(iteam.colors[1], iteam.colors[3]),
            labels = c("HMT", "Median Income"))

Below is a table comparing the two definitions based on the number of block groups and population in each tier. About 55% of Baltimore residents live in a middle neighborhood based on the HMT definition. However, only about 14% of the city’s population makes between 75% and 125% of AMI. 75% of the city population lives in a block group with median income below 75% of AMI. (Use the right triangle arrow at the right side of the table heading to see additional columns.)

hmt.summary <- hmt@data %>% 
  group_by(hmt.tier) %>%
  summarise(n.block.groups.hmt = n(),
            pop.hmt = sum(Population.2016)) %>%
  mutate(pct.block.groups.hmt = percent(n.block.groups.hmt / sum(n.block.groups.hmt)),
         pct.pop.hmt = percent(pop.hmt / sum(pop.hmt)))
med.inc.summmary <- hmt@data %>% 
  group_by(med.inc.tier) %>%
  summarise(n.block.groups.med.inc = n(),
            pop.med.inc = sum(Population.2016)) %>%
  mutate(pct.block.groups.med.inc = percent(n.block.groups.med.inc / sum(n.block.groups.med.inc)),
         pct.pop.med.inc = percent(pop.med.inc / sum(pop.med.inc)))
left_join(hmt.summary, med.inc.summmary, by = c("hmt.tier" = "med.inc.tier")) %>%
  rename(tier = "hmt.tier") %>%
  filter(tier != "other")

Incomes in HMT Middle Neighborhoods

The table below shows the combination of neighborhood housing market tier (based on HMT) and median income levels in each market tier (below 75% AMI, between 75% and 125% AMI, more than 125% AMI).

AMI used is $86,700 based on HUD’s FY16 figure for Baltimore-Columbia-Towson. For FY18, the number is $94,400, however the median income data being used in this analysis is the 5-year ACS estimates ending in 2016.

hmt@data <- hmt@data %>% mutate(
  
  inc.bracket = case_when(
    Median_Household_Income.2016 > 1.25 * ami.2016 ~ "above 125% AMI",
    Median_Household_Income.2016 < 0.75 * ami.2016 ~ "below 75% AMI",
    TRUE ~ "between 75% and 125% AMI"),
  
  tier.inc.bracket = as.character(paste0(hmt.tier.2mid, " | ", inc.bracket))
  
)
hmt@data %>%
  group_by(tier.inc.bracket) %>%
  summarise(n.block.groups = n(),
            pop = sum(Population.2016)) %>%
  mutate(pct.city.pop = percent(pop / sum(pop)))

Interesting to note that all housing market tiers include the low and middle median income ranges, but only the healthy HMT tier includes block groups with median incomes over 125% of AMI.

Here’s an alarming nugget: 55% of the city population is in a middle neighborhood , and 90% of the middle neighborhood population lives in a block group with a median income less than 75% AMI:

hmt@data %>%
  filter(hmt.tier == "middle") %>%
  group_by(tier.inc.bracket) %>%
  summarise(n.block.groups = n(),
            pop = sum(Population.2016)) %>%
  mutate(pct = percent(pop / sum(pop)))

Middle Neighborhoods with Low or High Incomes

(No middle neighborhood block groups with >125% AMI.)

tier.income.subset <- subset(hmt,  
                             hmt.tier.2mid %in% c("lower middle", "upper middle") &
                             inc.bracket %in% c("above 125% AMI", "below 75% AMI") )
tier.inc.pal <- colorFactor(
  domain = tier.income.subset$tier.inc.bracket,
  palette = c(iteam.colors[1],
              iteam.colors[3])
)
leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = tier.income.subset, 
              color = iteam.colors[2],
              weight = 1,
              fillColor = ~tier.inc.pal(tier.inc.bracket),
              #fillColor = iteam.colors[1],
              fillOpacity = 0.7,
              #opacity = 0,
              label = ~as.character(paste0(tier.income.subset$tier.inc.bracket))
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods,
             weight = 2,
             color = "black",
             opacity = 0.5,
             fillOpacity = 0,
             label = ~hoods$label) %>%
  addLegend("bottomright",
            colors = c(iteam.colors[1], iteam.colors[3]),
            labels = c("lower middle | below 75% AMI", "upper middle | below 75% AMI"))

Healthy and Distressed Neighborhoods with Moderate Income

tier.income.subset <- subset(
  hmt,  
  tier.inc.bracket %in% c("distressed | between 75% and 125% AMI",
                          "healthy | between 75% and 125% AMI")
)
tier.inc.pal <- colorFactor(
  domain = tier.income.subset$tier.inc.bracket,
  palette = c(iteam.colors[4],
              iteam.colors[5])
)
leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = tier.income.subset, 
              color = iteam.colors[2],
              weight = 1,
              fillColor = ~tier.inc.pal(tier.inc.bracket),
              #fillColor = iteam.colors[1],
              fillOpacity = 0.7,
              #opacity = 0,
              label = ~as.character(paste0(tier.income.subset$tier.inc.bracket))
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods,
             weight = 2,
             color = "black",
             opacity = 0.5,
             fillOpacity = 0,
             label = ~hoods$label) %>%
  addLegend("bottomright",
            colors = c(iteam.colors[4], iteam.colors[5]),
            labels = c("distressed | between 75% and 125% AMI", 
                       "healthy | between 75% and 125% AMI"))

Healthy Neighborhoods with Low Median Income

tier.income.subset <- subset(
  hmt,  
  tier.inc.bracket %in% c("healthy | below 75% AMI")
)
tier.inc.pal <- colorFactor(
  domain = tier.income.subset$tier.inc.bracket,
  palette = c(iteam.colors[5])
)
leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = tier.income.subset, 
              color = iteam.colors[2],
              weight = 1,
              fillColor = ~tier.inc.pal(tier.inc.bracket),
              #fillColor = iteam.colors[1],
              fillOpacity = 0.7,
              #opacity = 0,
              label = ~as.character(paste0(tier.income.subset$tier.inc.bracket))
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods,
             weight = 2,
             color = "black",
             opacity = 0.5,
             fillOpacity = 0,
             label = ~hoods$label) %>%
  addLegend("bottomright",
            colors = c(iteam.colors[5]),
            labels = c("healthy | below 75% AMI"))

Characteristics of Middle Neighborhoods

Using the HMT definition for middle neighborhoods, the following are looks at block group characteristics grouped by tier.

Because the HMT uses a cluster analysis that includes several housing variables, we’d expect there to be an overlap in the median income distributions because the tiers were not defined by median income.

hmt@data %>% 
  filter(hmt.tier != "other",
         !is.na(Median_Household_Income.2016)) %>% 
  ggplot(aes(Median_Household_Income.2016)) +
  geom_freqpoly(aes(color = hmt.tier), bins = 50) +
  theme_iteam_presentations() +
  labs(
    title = paste0("Block Group Median Household Income\n",
                   "Distribution by HMT Market tier"),
    subtitle = "",
    x = "Median Household Income",
    y = "Count",
    caption = paste0("tier based on 2017 HMT analysis; ",
                     "median household incomes from 2012-2016 ACS data.")
  ) +
  geom_vline(aes(xintercept = ami.2016), color = iteam.colors[2]) +
  annotate("text", x = ami.2016 + 2500, y = 25, 
           label = paste0("Baltimore-Columbia-Towson 2016 AMI:\n", dollar(ami.2016)),
           hjust = 0) +
  theme(plot.caption = element_text(hjust = 1, face = "plain"))

Difference in median incomes between upper and lower middle neighborhoods.

hmt@data %>% 
  filter(hmt.tier != "other",
         !is.na(Median_Household_Income.2016),
         hmt.tier == "middle") %>% 
  ggplot(aes(Median_Household_Income.2016)) +
  geom_freqpoly(aes(color = hmt.tier.2mid), bins = 50) +
  theme_iteam_presentations() +
  labs(
    title = paste0("Middle Neighborhood\nBlock Group Median Household Income\n",
                   "Distribution by HMT Market tier"),
    subtitle = "",
    x = "Median Household Income",
    y = "Count",
    caption = paste0("tier based on 2017 HMT analysis; ",
                     "median household incomes from 2012-2016 ACS data.")
  ) +
  geom_vline(aes(xintercept = ami.2016), color = iteam.colors[2]) +
  annotate("text", x = ami.2016 - 2500, y = 16, 
           label = paste0("Baltimore-Columbia-Towson 2016 AMI:\n", dollar(ami.2016)),
           hjust = 1) +
  theme(plot.caption = element_text(hjust = 1, face = "plain"),
        legend.title = element_blank())

Middle neighborhoods do seem to skew towards older homeownership.

hmt@data %>% 
  filter(hmt.tier != "other") %>% 
  ggplot(aes(Percent_Homeowner_Over_65.2016)) +
  geom_freqpoly(aes(color = hmt.tier), stat = "density") +
  theme_iteam_presentations() +
  labs(
    title = "% Homeowners 65+ by HMT Market tier",
    subtitle = "",
    x = "% Homeowners 65+ y.o.",
    y = "Density",
    caption = paste0("tier based on 2017 HMT analysis; ",
                     "% homeowners 65 and over from 2012-2016 ACS data.")
  ) +
  theme(plot.caption = element_text(hjust = 1, face = "plain"))

Below is the average block group percentage of homeowners that are over the age of 65.

hmt@data %>%
  filter(hmt.tier != "other") %>%
  group_by(hmt.tier) %>%
  summarise(mean.pct.ho.over65 = mean(Percent_Homeowner_Over_65.2016))

While about 54% of the city population lives in middle neighborhoods, 65% of the households with a homeowner 65 years of age or older live in a middle neighborhood.

hmt@data %>%
  filter(hmt.tier != "other") %>%
  mutate(hh = Population.2016 / Household_Size.2016,
         hh.own = hh * Percent_Owner.2016,
         hh.owner.over65 = hh.own * Percent_Homeowner_Over_65.2016) %>%
  group_by(hmt.tier) %>%
  summarise(hh.owner.over65 = sum(hh.owner.over65)) %>%
  mutate(pct.city.owner.over65 = percent(hh.owner.over65 / sum(hh.owner.over65)))
---
title: "What is a Middle Neighborhood?"
#subtitle: 
author: "Justin Elszasz"
date: "February 3, 2019"
output:
  html_notebook:
    toc: yes
    toc_depth: 2
    toc_float: false
    code_folding: hide
---

This is a brief comparison of definitions for "middle neighborhoods" in Baltimore. The two definitions being compared are:

- U.S. Census block groups with **housing market typologies D through H** based on the [2017 Housing Market Typology](https://planning.baltimorecity.gov/maps-data/housing-market-typology) analysis (healthy = A, B, C; distressed = I, J)
- U.S. Census block groups with **median household income between 75% and 125% of the city's area median income**

U.S. Census data being used are the 2012-2016 (5-year) American Community Survey estimates.

Are there any other definitions we should consider?

```{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.width=8, fig.height=6,
                      echo=F, warning=F, message=F, include=T)
```

```{r include = F}
library(readxl)
#library(RODBC)
library(maptools)
library(leaflet)
library(rgdal)
library(rgeos)
library(sf)
library(httr)
library(jsonlite)
library(geojsonsf)
library(raster)
```

```{r include=F}
source("../code/00_initialize.R")

hmt <- get_block_group_data()
# pull HMT layer from SQL server
# server <-   paste0('MSSQL:server=balt-gslistener;',
#                    'uid=egis_reader;',
#                    'pwd=baltimore01;',
#                    'database=housing;',
#                    'trusted_connection=No')
# 
# hmt <- readOGR(server, "housing.HMT2017")
# 
# hmt <- SpatialPolygonsDataFrame(hmt, hmt@data)
# hmt@proj4string <- CRS("+init=epsg:2248")
# hmt <- spTransform(hmt, CRS("+init=epsg:4326"))
```

```{r}
# neighborhood boundaries
hoods.url <- "https://data.baltimorecity.gov/resource/h3fx-54q3.geojson"

# surely there is a better way, and i tried, but couldn't get anything else to work.
# from geojson to sf to spatial df. geojson_sp didn't work for me.
hoods <- geojson_sf(hoods.url)
hoods <- as_Spatial(hoods)
hoods <- spTransform(hoods, CRS("+init=epsg:4326"))
```

```{r}
# HUD area median income for Baltimore-Columbia-Towson
ami.2016 <- 86700
#ami.2018 <- 94900

hmt@data <- hmt@data %>% mutate(
  
  # assign healthy, middle, and distressed based on HMT
  hmt.tier = case_when(
    MVA17HrdCd %in% c("A", "B", "C") ~ "healthy",
    MVA17HrdCd %in% c("D", "E", "F", "G", "H") ~ "middle",
    MVA17HrdCd %in% c("I", "J") ~ "distressed",
    TRUE ~ "other"),
  
  # HMT again, but with middle broken in two
  hmt.tier.2mid = case_when(
    MVA17HrdCd %in% c("A", "B", "C") ~ "healthy",
    MVA17HrdCd %in% c("D", "E") ~ "upper middle",
    MVA17HrdCd %in% c("F", "G", "H") ~ "lower middle",
    MVA17HrdCd %in% c("I", "J") ~ "distressed",
    TRUE ~ "other"),

  # assign healthy, middle, and distressed based on block group median income
    med.inc.tier = 
    case_when(
      (hmt.tier != "other") & 
        (Median_Household_Income.2016 > 1.25 * ami.2016) ~ "healthy",
      (hmt.tier != "other") & 
        (Median_Household_Income.2016 < 0.75 * ami.2016) ~ "distressed",
      (hmt.tier == "other") ~ NA_character_,
      TRUE ~ "middle"
    )
  )
```

# Middle Neighborhoods - HMT Definiton

This map shows only middle neighborhoods based on HMT (includes only typologies D through H). Black borders and labels-on-hover are for neighborhoods. At first glance, this removes neighborhoods within the "white L" and "black butterfly" neighborhoods of Baltimore, with most everything else remaining.

```{r}
mid.hoods.hmt <- subset(hmt, hmt.tier == "middle")

leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = mid.hoods.hmt, 
              color = iteam.colors[1],
              weight = 1,
              #fillColor = ~pal(MVA17HrdCd),
              fillColor = iteam.colors[1],
              fillOpacity = .6,
              #opacity = 0,
              label = ~as.character(mid.hoods.hmt$MVA17HrdCd)
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~hoods$label)
```

Breaking "middle" into "lower middle" (F, G, H) and "upper middle" (D, E) and including "healthy" and "distressed:

```{r}
#mid.hoods.hmt.2mid <- subset(hmt, hmt.tier.2mid %in% c("lower middle", "upper middle"))

hmt.2mid.pal <- colorFactor(
  domain = hmt$hmt.tier.2mid,
  palette = c(iteam.colors[4],
              iteam.colors[3],
              "#f4d57f", 
              NA,
              iteam.colors[1])
)

leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = hmt, 
              color = iteam.colors[1],
              weight = 1,
              fillColor = ~hmt.2mid.pal(hmt.tier.2mid),
              #fillColor = iteam.colors[1],
              fillOpacity = .6,
              #opacity = 0,
              label = ~as.character(hmt$MVA17HrdCd)
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~hoods$label)
```


# Middle Neighborhoods - Median Income Definition

This map shows only middle neighborhoods based on the median income definition - that is, block groups where the household median income is between 75% and 125% of the area median income ([Baltimore-Columbia-Towson for 2016](https://www.huduser.gov/portal/datasets/il.html#2016)). Qualitatively, this results in a more sparse and disjointed definition of middle neighborhoods. 

```{r}
mid.hoods.med.inc <- subset(hmt, med.inc.tier == "middle")

leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = mid.hoods.med.inc, 
              color = iteam.colors[3],
              weight = 1,
              #fillColor = ~pal(MVA17HrdCd),
              fillColor = iteam.colors[3],
              fillOpacity = .6,
              #opacity = 0,
              label = ~as.character(mid.hoods.med.inc$MVA17HrdCd)
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~hoods$label)
```

# Comparison of Definitions

The map below overlays the two definitions.

```{r}
leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = mid.hoods.med.inc, 
              color = iteam.colors[3],
              weight = 1,
              #fillColor = ~pal(MVA17HrdCd),
              fillColor = iteam.colors[3],
              fillOpacity = .7,
              #opacity = 0,
              label = ~as.character(mid.hoods.med.inc$MVA17HrdCd)) %>%
  addPolygons(data = mid.hoods.hmt, 
              color = iteam.colors[1],
              weight = 1,
              #fillColor = ~pal(MVA17HrdCd),
              fillColor = iteam.colors[1],
              fillOpacity = .5,
              #opacity = 0,
              label = ~as.character(mid.hoods.hmt$MVA17HrdCd)) %>%
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~hoods$label) %>%
  addLegend("bottomright",
            colors = c(iteam.colors[1], iteam.colors[3]),
            labels = c("HMT", "Median Income"))
```

Below is a table comparing the two definitions based on the number of block groups and population in each tier. About 55% of Baltimore residents live in a middle neighborhood based on the HMT definition. However, only about 14% of the city's population makes between 75% and 125% of AMI. **75% of the city population lives in a block group with median income below 75% of AMI.** (Use the right triangle arrow at the right side of the table heading to see additional columns.)

```{r}
hmt.summary <- hmt@data %>% 
  group_by(hmt.tier) %>%
  summarise(n.block.groups.hmt = n(),
            pop.hmt = sum(Population.2016)) %>%
  mutate(pct.block.groups.hmt = percent(n.block.groups.hmt / sum(n.block.groups.hmt)),
         pct.pop.hmt = percent(pop.hmt / sum(pop.hmt)))

med.inc.summmary <- hmt@data %>% 
  group_by(med.inc.tier) %>%
  summarise(n.block.groups.med.inc = n(),
            pop.med.inc = sum(Population.2016)) %>%
  mutate(pct.block.groups.med.inc = percent(n.block.groups.med.inc / sum(n.block.groups.med.inc)),
         pct.pop.med.inc = percent(pop.med.inc / sum(pop.med.inc)))

left_join(hmt.summary, med.inc.summmary, by = c("hmt.tier" = "med.inc.tier")) %>%
  rename(tier = "hmt.tier") %>%
  filter(tier != "other")
```


# Incomes in HMT Middle Neighborhoods

The table below shows the combination of neighborhood housing market tier (based on HMT) and median income levels in each market tier (below 75% AMI, between 75% and 125% AMI, more than 125% AMI).

AMI used is \$86,700 based on HUD's FY16 figure for Baltimore-Columbia-Towson. For FY18, the number is \$94,400, however the median income data being used in this analysis is the 5-year ACS estimates ending in 2016.



```{r}
hmt@data <- hmt@data %>% mutate(
  
  inc.bracket = case_when(
    Median_Household_Income.2016 > 1.25 * ami.2016 ~ "above 125% AMI",
    Median_Household_Income.2016 < 0.75 * ami.2016 ~ "below 75% AMI",
    TRUE ~ "between 75% and 125% AMI"),
  
  tier.inc.bracket = as.character(paste0(hmt.tier.2mid, " | ", inc.bracket))
  
)

hmt@data %>%
  group_by(tier.inc.bracket) %>%
  summarise(n.block.groups = n(),
            pop = sum(Population.2016)) %>%
  mutate(pct.city.pop = percent(pop / sum(pop)))
```

Interesting to note that all housing market tiers include the low and middle median income ranges, but only the healthy HMT tier includes block groups with median incomes over 125% of AMI.

Here's an alarming nugget: 55% of the city population is in a middle neighborhood , and **90% of the middle neighborhood population** lives in a block group with a median income less than 75% AMI:

```{r}
hmt@data %>%
  filter(hmt.tier == "middle") %>%
  group_by(tier.inc.bracket) %>%
  summarise(n.block.groups = n(),
            pop = sum(Population.2016)) %>%
  mutate(pct = percent(pop / sum(pop)))
```

### Middle Neighborhoods with Low or High Incomes

(No middle neighborhood block groups with >125% AMI.)

```{r}
tier.income.subset <- subset(hmt,  
                             hmt.tier.2mid %in% c("lower middle", "upper middle") &
                             inc.bracket %in% c("above 125% AMI", "below 75% AMI") )

tier.inc.pal <- colorFactor(
  domain = tier.income.subset$tier.inc.bracket,
  palette = c(iteam.colors[1],
              iteam.colors[3])
)

leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = tier.income.subset, 
              color = iteam.colors[2],
              weight = 1,
              fillColor = ~tier.inc.pal(tier.inc.bracket),
              #fillColor = iteam.colors[1],
              fillOpacity = 0.7,
              #opacity = 0,
              label = ~as.character(paste0(tier.income.subset$tier.inc.bracket))
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods,
             weight = 2,
             color = "black",
             opacity = 0.5,
             fillOpacity = 0,
             label = ~hoods$label) %>%
  addLegend("bottomright",
            colors = c(iteam.colors[1], iteam.colors[3]),
            labels = c("lower middle | below 75% AMI", "upper middle | below 75% AMI"))
```

### Healthy and Distressed Neighborhoods with Moderate Income

```{r}
tier.income.subset <- subset(
  hmt,  
  tier.inc.bracket %in% c("distressed | between 75% and 125% AMI",
                          "healthy | between 75% and 125% AMI")
)

tier.inc.pal <- colorFactor(
  domain = tier.income.subset$tier.inc.bracket,
  palette = c(iteam.colors[4],
              iteam.colors[5])
)

leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = tier.income.subset, 
              color = iteam.colors[2],
              weight = 1,
              fillColor = ~tier.inc.pal(tier.inc.bracket),
              #fillColor = iteam.colors[1],
              fillOpacity = 0.7,
              #opacity = 0,
              label = ~as.character(paste0(tier.income.subset$tier.inc.bracket))
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods,
             weight = 2,
             color = "black",
             opacity = 0.5,
             fillOpacity = 0,
             label = ~hoods$label) %>%
  addLegend("bottomright",
            colors = c(iteam.colors[4], iteam.colors[5]),
            labels = c("distressed | between 75% and 125% AMI", 
                       "healthy | between 75% and 125% AMI"))
```


### Healthy Neighborhoods with Low Median Income

```{r}
tier.income.subset <- subset(
  hmt,  
  tier.inc.bracket %in% c("healthy | below 75% AMI")
)

tier.inc.pal <- colorFactor(
  domain = tier.income.subset$tier.inc.bracket,
  palette = c(iteam.colors[5])
)

leaflet() %>% 
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(data = tier.income.subset, 
              color = iteam.colors[2],
              weight = 1,
              fillColor = ~tier.inc.pal(tier.inc.bracket),
              #fillColor = iteam.colors[1],
              fillOpacity = 0.7,
              #opacity = 0,
              label = ~as.character(paste0(tier.income.subset$tier.inc.bracket))
              ) %>%
  #addGeoJSON(hoods, weight = 1, color = "black", fillOpacity = 0)
  addPolygons(data = hoods,
             weight = 2,
             color = "black",
             opacity = 0.5,
             fillOpacity = 0,
             label = ~hoods$label) %>%
  addLegend("bottomright",
            colors = c(iteam.colors[5]),
            labels = c("healthy | below 75% AMI"))

```


# Characteristics of Middle Neighborhoods

Using the HMT definition for middle neighborhoods, the following are looks at block group characteristics grouped by tier.

Because the HMT uses a cluster analysis that includes several housing variables, we'd expect there to be an overlap in the median income distributions because the tiers were not defined by median income.

```{r}
hmt@data %>% 
  filter(hmt.tier != "other",
         !is.na(Median_Household_Income.2016)) %>% 
  ggplot(aes(Median_Household_Income.2016)) +
  geom_freqpoly(aes(color = hmt.tier), bins = 50) +
  theme_iteam_presentations() +
  labs(
    title = paste0("Block Group Median Household Income\n",
                   "Distribution by HMT Market tier"),
    subtitle = "",
    x = "Median Household Income",
    y = "Count",
    caption = paste0("tier based on 2017 HMT analysis; ",
                     "median household incomes from 2012-2016 ACS data.")
  ) +
  geom_vline(aes(xintercept = ami.2016), color = iteam.colors[2]) +
  annotate("text", x = ami.2016 + 2500, y = 25, 
           label = paste0("Baltimore-Columbia-Towson 2016 AMI:\n", dollar(ami.2016)),
           hjust = 0) +
  theme(plot.caption = element_text(hjust = 1, face = "plain"))
```

Difference in median incomes between upper and lower middle neighborhoods.

```{r}
hmt@data %>% 
  filter(hmt.tier != "other",
         !is.na(Median_Household_Income.2016),
         hmt.tier == "middle") %>% 
  ggplot(aes(Median_Household_Income.2016)) +
  geom_freqpoly(aes(color = hmt.tier.2mid), bins = 50) +
  theme_iteam_presentations() +
  labs(
    title = paste0("Middle Neighborhood\nBlock Group Median Household Income\n",
                   "Distribution by HMT Market tier"),
    subtitle = "",
    x = "Median Household Income",
    y = "Count",
    caption = paste0("tier based on 2017 HMT analysis; ",
                     "median household incomes from 2012-2016 ACS data.")
  ) +
  geom_vline(aes(xintercept = ami.2016), color = iteam.colors[2]) +
  annotate("text", x = ami.2016 - 2500, y = 16, 
           label = paste0("Baltimore-Columbia-Towson 2016 AMI:\n", dollar(ami.2016)),
           hjust = 1) +
  theme(plot.caption = element_text(hjust = 1, face = "plain"),
        legend.title = element_blank())
```

Middle neighborhoods do seem to skew towards older homeownership.

```{r}
hmt@data %>% 
  filter(hmt.tier != "other") %>% 
  ggplot(aes(Percent_Homeowner_Over_65.2016)) +
  geom_freqpoly(aes(color = hmt.tier), stat = "density") +
  theme_iteam_presentations() +
  labs(
    title = "% Homeowners 65+ by HMT Market tier",
    subtitle = "",
    x = "% Homeowners 65+ y.o.",
    y = "Density",
    caption = paste0("tier based on 2017 HMT analysis; ",
                     "% homeowners 65 and over from 2012-2016 ACS data.")
  ) +
  theme(plot.caption = element_text(hjust = 1, face = "plain"))
```

Below is the average block group percentage of homeowners that are over the age of 65.

```{r}
hmt@data %>%
  filter(hmt.tier != "other") %>%
  group_by(hmt.tier) %>%
  summarise(mean.pct.ho.over65 = mean(Percent_Homeowner_Over_65.2016),
            med.pct.ho.over65 = median(Percent_Homeowner_Over_65.2016))
```

While about 54% of the city population lives in middle neighborhoods, 65% of the households with a homeowner 65 years of age or older live in a middle neighborhood.

```{r}
hmt@data %>%
  filter(hmt.tier != "other") %>%
  mutate(hh = Population.2016 / Household_Size.2016,
         hh.own = hh * Percent_Owner.2016,
         hh.owner.over65 = hh.own * Percent_Homeowner_Over_65.2016) %>%
  group_by(hmt.tier) %>%
  summarise(hh.owner.over65 = sum(hh.owner.over65)) %>%
  mutate(pct.city.owner.over65 = percent(hh.owner.over65 / sum(hh.owner.over65)))
```
```{r}
lm.data <- hmt@data %>% filter(Median_Household_Income.2016 != 0)
lm.model <- lm(MSP1517eo ~ Median_Household_Income.2016, data = lm.data)

lm.model

library(plotly)

inc.price <- hmt@data %>%
  ggplot(aes(Median_Household_Income.2016, MSP1517eo)) +
  geom_point() +
  theme_iteam_presentations() +
  geom_abline(aes(intercept = lm.model$coefficients[1], 
                  slope = lm.model$coefficients[2])) +
  xlab("Median Household Income") +
  ylab("Median Sales Price")

ggplotly(inc.price)
  

summary(lm.model)
```




